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Objective:To screen out the differentially regulated metabolites by the analysis of serum metabolic fingerprints, and to provide potential biomarkers for diagnosis of lung cancer.Methods:A total of 228 subjects were enrolled in Changhai Hospital from January 27, 2021 to June 4, 2021, including 97 newly diagnosed lung cancer patients and 131 healthy individuals. Serum samples were collected from the enrolled cohort according to a standard procedure, and the enrolled cohort was divided into a training set and a completely independent validation set by stratified random sampling. The metabolic fingerprints of serum samples were collected by previously developed nano-assisted laser desorption/ionization mass spectrometry (nano-LDI MS). After age and gender matching of the training set, a diagnostic model based on serum metabolic fingerprints was established by machine learning algorithm, and the classification performance of the model was evaluated by receiver operating characteristic (ROC) curve.Results:Serum metabolic fingerprint for each sample was obtained in 1 minute using a novel nano-LDI MS, with consumption of only 1 μl original serum sample. For the training set, the area under ROC curve (AUC) of the constructed classifier for diagnosis of lung cancer was 0.92 (95% CI 0.87-0.97), with a sensitivity of 89% and specificity of 89%. For the independent validation set, the AUC reached 0.96 (95% CI 0.90-1.00) with a sensitivity of 91% and specificity of 94%, which showed no significant decrease compared to training set. We also identified a biomarker panel of 5 metabolites, demonstrating a unique metabolic fingerprint of lung cancer patients. Conclusion:Serum metabolic fingerprints and machine learning were combined to establish a diagnostic model, which can be used to distinguish between lung cancer patients and healthy controls. This work sheds lights on the rapid metabolic analysis for clinical application towards in vitro diagnosis.
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Objective:We aimed to explore a colorectal cancer risk prediction model through machine learning algorithm based on the big data in laboratory medicine.Methods:According to the labeling of colonoscopy combined with pathology or referring to the ICD-10 code, the colonoscopy patients in Shanghai Changhai Hospital from 2013.1.1 to 2019.6.30 and the outpatients and inpatients from 2010.1.1 to 2019.6.30 were divided into colorectal cancer groups and non-colorectal cancer group. Four machine learning algorithms, Extreme gradient boosting(Xgboost),Artificial Neural Network(ANN),Support Vector Machine(SVM),Random Forest(RF), are used to mine all routine laboratory test item data of the enrolled patients, select model features and establish a classification model for colorectal cancer. And the effectiveness of the model was prospectively verified in patients in the whole hospital of Changhai Hospital from 2019.7.1 to 2020.8.31.Result:A colorectal cancer risk prediction model (CRC-Lab7) including 7 characteristics of fecal occult blood, carcinoembryonic antigen, red blood cell distribution width, lymphocyte count, albumin/globulin, high-density lipoprotein cholesterol and hepatitis B virus core antibody was constructed by the XgBoost algorithm. The AUC of the model in the validation set and prospective validation set were 0.799 and 0.816, respectively, which was significantly higher than that of fecal occult blood (AUC was 0.68 and 0.706, respectively). It also has high diagnostic accuracy for colorectal cancer with negative fecal occult blood or under 50 years old.Conclusion:In this study, a colorectal cancer risk prediction model was established by mining routine laboratory big data. The model′s performance is better than fecal occult blood, and it has high diagnostic accuracy for colorectal cancer in patients with negative fecal occult blood and younger than 50 years old.
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Research on the application of artificial intelligence in laboratory medicine has become an important direction for laboratory development. However, there were still problems in the process of product application research and development of artificial intelligence technology, such as lack of interpretability of machine learning models, lack of talent teams, and many potential safety hazards. The reason for this may include low quality of data sets, deviations in research design, imperfect talent training mechanism, and inadequate legislation and supervision. In response to these reasons, the article proposes countermeasures, including establishing data entry and collection standards, formulating data labeling management standards, making model risk analysis, strengthening compound talent training, and improving supervision and management systems. Ensuring artificial intelligence products applied in the field of laboratory medicine could effectively improve the quality of medical services on the premise of improving the efficiency of diagnosis and reducing the rate of misdiagnosis and missed diagnosis.
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Objective:To evaluate the feasibility of a predictire model composed of non-specific test indexes in early diagnosis of gastric cancer.Methods:From the database of electronic medical record system of Shanghai Changhai Hospital, a total of 24 615 case records were included from January 1, 2010 to April 30, 2019, including 10 497 cases of gastric cancer, 5 198 cases of precancerous diseases, and 8 920 cases of health examination. Through stratified random sampling, the study population was divided into validation set, training set and test set. After data processing and quality control for all laboratory variables, the optimal machine learning algorithm and diagnostic efficiency grouping were selected through four machine learning algorithms, induding the gradient boosting decision tree, random forest, support vector machine, and artificial neural network, and the data were trained by backward stepwise regression method to build the best feature model.Result:In this study, a diagnostic model V22 consisting of 22 routine testing parameters was established. V22 could distinguish early gastric cancer from control group composed of healthy group and precancerous disease, AUC was 0.808, the sensitivity was 85.7%, and the specificity was 91.9%. For CEA negative gastric cancer, V22 also showed high diagnostic accuracy, AUC was 0.801.Conclusion:V22 was a valuable model for the diagnosis of gastric cancer. V22 was an auxiliary diagnostic model of gastric cancer with clinical application value, which could well distinguish early gastric cancer from the control group composed of healthy group and precancerous disease, and the detection rate of early gastric cancer was better than the traditional tumor marker CEA.
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The modern laboratory developed tests (LDTs) plays a very important role in the development of precision medicine and clinical laboratory diagnosis. The perfect LDTs inspection platform not only requires strict quality control system and management standards, but also requires technical support from high-end professional teams. By combing the development process of LDTs, this paper discusses the significance of developing LDTs in the context of precision medicine, the positive role played by the development of laboratory medicine and the cultivation of laboratory talents, and how to effectively develop LDTs.
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There's a very complicated association between gut microbiota and diseases. With the development of metagenomics and bioinformatics, people have a new recognition of its role in pathogenesis, diagnosis and treatments. This review focuses on the close relationship between gut microbiota and liver diseases, analyzes and discusses the current research status, value of clinical applications and development directions. It is suggested that investigating the interactions between gut microbiota and body to discover new pathogenic pathway, maybe helpful in seeking for relevant drug targets and developing more sensitive early detection markers, then undoubtedly facilitate the clinical diagnosis and treatment of liver diseases.
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The modern laboratory developed tests (LDTs) plays a very important role in the development of precision medicine and clinical laboratory diagnosis. The perfect LDTs inspection platform not only requires strict quality control system and management standards, but also requires technical support from high-end professional teams. By combing the development process of LDTs, this paper discusses the significance of developing LDTs in the context of precision medicine, the positive role played by the development of laboratory medicine and the cultivation of laboratory talents, and how to effectively develop LDTs.
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Real world study has attracted more and more researchers' attention because of the evidence obtained from real clinical practice. With the development of big data in laboratory medicine and the needs for translational studies for diagnostic markers, the concept of real world study has also been introduced in laboratory medicine. This article presents a preliminary discussion on the opportunities and requirements for the real world study in the development of laboratory medicine, and on how the researchers conduct real world study.
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Circulating tumor cells (CTCs) have shown a prognostic value in clinical tumor diagnosis, CTCs and related liquid biopsy technique have become a new hot focus for clinical diagnosis of tumor study in recent years.This review evaluates the limitation of CTCs′ applications in clinical studies and makes some suggestions for the future clinical application in order to promote the development of liquid biopsy technique.
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Precision medicine is an emerging approach for disease treatment and prevention , which attempts to explore the effective means for protecting human health by synthetical consideration of individual variability in genes , environment and life style.Precision medicine has the prominent property of multidisciplinary intercrossing and fusion , the development of which claims rapid clinical application of advanced technology in the research of basic medical science.What kind of development opportunities are laboratory medicine confronted with under the novel medical mode? How can laboratory medicine and its researchers do to seize these opportunities and meet the challenges of precision medicine ? These questions are preliminary discussed in this paper.
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Cancer stem cells (CSCs) are thought to drive uncontrolled tumor growth, and the existence of CSCs has recently been proven by direct experimental evidence, including tracing cell lineages within a growing tumor. However, CSCs must be analyzed in additional cancer types. Cancer stem cell-like cells (CSCLCs) are a good alternative system for the study of CSCs, which hold great promise for clinical applications. OCT4, NANOG, and SOX2 are three basic transcription factors that are expressed in both CSCLCs and embryonic stem cells (ESCs). These transcription factors play critical roles in maintaining the pluripotence and self-renewal characteristics of CSCLCs and ESCs. In this review, we discuss the aberrant expression, isoforms, and pseudogenes of OCT4, NANOG, and SOX2 in the CSCLC niche, which contribute to the major differences between CSCLCs and ESCs. We also highlight an anticancer therapy that involves killing specific cancer cells directly by repressing the expression of OCT4, NANOG, or SOX2. Importantly, OCT4, NANOG, and SOX2 provide great promise for clinical applications because reducing their expression or blocking the pathways in which they function may inhibit tumor growth and turn-off the cancer "switch." In the future, a clear understanding of transcription factor regulation will be essential for elucidating the roles of OCT4, NANOG, and SOX2 in tumorigenesis, as well as exploring their use for diagnostic and therapeutic purposes.
Subject(s)
Animals , Humans , Embryonic Stem Cells , Metabolism , Homeodomain Proteins , Metabolism , Nanog Homeobox Protein , Neoplasms , Metabolism , Pathology , Neoplastic Stem Cells , Metabolism , Octamer Transcription Factor-3 , Metabolism , SOXB1 Transcription Factors , Metabolism , Signal TransductionABSTRACT
Presently, there are 3 problems in the study of human embryonic pluripotent stem cells: short of materials, complicacy of culture system and spontaneous differentiation. Both basic research and clinical application of human embryonic pluripotent stem cells were hindered by these 3 basic problems. New embryonic pluripotent stem cells should be established by combining nucleus transfer and plasma reprograming, and their possible mechanisms and key factors need further study. The culture system should be simplified and cell differentiation must be inhibited when pluripotent stem cell is cultured. When pluripotent stem cells are induced to differentiation, combination of several methods should be used to induce and select specific cell lineages, and the morphology and function of the target cell must be evaluated.
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The germ cell lineage in the mouse is established during gastrulation;sex determination is achieved after germ cell migrating to the site of the future gonads. Germ cells proliferate indefinitely when cultured in vitro . Human embryonic germ(EG) cells have been recently established;these immortalized EG cells are chromosomally stable and pluripotent, raising the hope that their differentiation can be directed to specific cell types, which may be of value in the clinical treatment of degenerative diseases.
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The Oct4,a POU transcription factor belongs to class Ⅴ of POU proteins, is expressed in mouse totipotent embryonic stem and germ cells. Oct4 plays a critical role in maintaining pluripotency of stem cell when it differentiates into a trophectoderm lineage at early stage of mouse embryo. Oct4 expression appears to be regulated by a positional effect as well as specific regulating elements of Oct4 such as proximal enhancer (PE) and distal enhancer (DE). Oct4 has both repression and activation effects in regulating transcription,and the activation has 3 models: distance dependent transactivation, conformational transactivation and Oct4 dimers dependent transactivation.
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All types of blood cells are derived from a common precursor:the hematopoietic stem cell.Hematopoietic stem cells can be found in different locations of the developing vertebrate organism. Hematopoiesis is a dynamic process sustained by cytokine induced production and activities of hematopoietic stem and progenitor cells.A number of different cytokines,cells,and cytokine cell interactions are involved in regulating the hematopoiesis. Some cytokines have more than one functions partially due to their actions on different target cells.These actions are mediated through specific receptors transmiting intracellular signals.These signals give the stem and progenitor cells critical information to survive,proliferate, differentiate or to migrate. This paper is to review recent developments in some of the major signal transduction pathways influencing hematopoietic growth and differentiation with a particular emphasis on myeloid cells.